qntz

Vector quantization primitives for approximate nearest neighbor search: RaBitQ (1-8 bit), ternary quantization, bit packing, and Hamming distance. Backed by a Rust implementation.

Python bindings for the qntz Rust crate.

Install

pip install qntz

Quick start

import qntz

# RaBitQ quantization (1-8 bits per dimension)
dim = 32
quantizer = qntz.RaBitQQuantizer(dim, seed=42, total_bits=4)
vector = [float(i) * 0.1 for i in range(dim)]
qv = quantizer.quantize(vector)
query = [float(i) * 0.05 for i in range(dim)]
dist = quantizer.approximate_distance(query, qv)

# Ternary quantization ({-1, 0, +1} per dimension)
tq = qntz.TernaryQuantizer(4, threshold_high=0.3, threshold_low=-0.3, normalize=False)
a = tq.quantize([0.5, -0.5, 0.1, -0.1])
b = tq.quantize([0.5, 0.5, 0.0, -0.5])
sim = qntz.ternary_cosine_similarity(a, b)

# Bit packing and Hamming distance
packed_a = qntz.pack_binary([1, 0, 1, 0, 1, 0, 1, 0])
packed_b = qntz.pack_binary([1, 1, 1, 0, 0, 0, 1, 0])
dist = qntz.hamming_distance(packed_a, packed_b)  # 2

API

Name	Description
`RaBitQQuantizer`	Quantizer using randomized Hadamard rotation, 1-8 bits/dim
`RaBitQQuantizer.fit`	Fit centroid from training vectors
`RaBitQQuantizer.quantize`	Quantize a single vector, returns `QuantizedVector`
`RaBitQQuantizer.approximate_distance`	Approximate Euclidean distance to a quantized vector
`RaBitQQuantizer.approximate_l2_sqr`	Approximate squared L2 distance to a quantized vector
`TernaryQuantizer`	Maps each dimension to {-1, 0, +1} via thresholds
`TernaryQuantizer.fit`	Fit adaptive thresholds from training vectors
`TernaryQuantizer.quantize`	Quantize a single vector, returns `TernaryVector`
`pack_binary`	Pack 0/1 values into a compact bitfield
`unpack_binary`	Unpack a bitfield back to one byte per bit
`hamming_distance`	Hamming distance between two packed bit-vectors
`asymmetric_inner_product`	f32 query vs packed 1-bit codes inner product
`asymmetric_l2_squared`	f32 query vs packed 1-bit codes L2 squared
`ternary_inner_product`	Inner product between two ternary vectors
`ternary_cosine_similarity`	Cosine similarity between two ternary vectors
`ternary_hamming`	Hamming distance between two ternary vectors
`ternary_asymmetric_inner_product`	f32 query vs ternary codes inner product
`ternary_asymmetric_cosine_distance`	f32 query vs ternary codes cosine distance

numpy support

All functions accept numpy arrays or Python lists. Packed outputs are numpy uint8 arrays.

License

MIT OR Apache-2.0

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Upload date: Jul 7, 2026
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- Subject name: qntz-0.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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- Sigstore integration time: Jul 7, 2026
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- Permalink: arclabs561/qntz@2a1936b6e3d2fd975bcf18b83e254810dad17619
- Branch / Tag: refs/tags/python-v0.1.3
- Owner: https://github.com/arclabs561
- Access: public
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- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish-pypi.yml@2a1936b6e3d2fd975bcf18b83e254810dad17619
- Trigger Event: push

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This release

0.1.3

Jul 7, 2026

0.1.2

Jul 7, 2026

0.1.1

Mar 22, 2026

0.1.0

Mar 21, 2026

qntz 0.1.3

qntz

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